Black Brands and Moral Affirmation
Jonathan Doriscar
2023-10-22
0. Beginning stuff
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options(scipen = 100)wf_dt <- read_csv("~/Documents/White Fragility/Buying Black and Moral Affirmation/wf_maS1.csv")## Rows: 327 Columns: 116
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (39): StartDate, EndDate, RecordedDate, ResponseId, DistributionChannel,...
## dbl (76): ParticipantID, Status, Progress, Duration (in seconds), Finished, ...
## num (1): race
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(wf_dt)
attach(wf_dt)colnames(wf_dt)## [1] "ParticipantID" "StartDate" "EndDate"
## [4] "Status" "Progress" "Duration (in seconds)"
## [7] "Finished" "RecordedDate" "ResponseId"
## [10] "DistributionChannel" "UserLanguage" "consent"
## [13] "prolificID" "BA_1" "BA_2"
## [16] "BA_3" "BA_4" "SE1"
## [19] "BMIS_sad_1" "BMIS_shame_1" "BMIS_guilt_1"
## [22] "BMIS_tired_1" "BMIS_nervous_1" "BMIS_calm_1"
## [25] "BMIS_fedup_1" "BMIS_loving_1" "BMIS_angry_1"
## [28] "BMIS_lively_1" "BMIS_caring_1" "BMIS_content_1"
## [31] "BMIS_gloomy_1" "BMIS_jittery_1" "BMIS_drowsy_1"
## [34] "BMIS_happy_1" "Q1 RP1" "Q2 RP2"
## [37] "Q3 RP3" "Q4 RP4" "Q5 RP5"
## [40] "Q6 RP6" "Q7 RP7" "Q8 RN1"
## [43] "Q9 RN2" "Q10 RN3" "Q11 RN4"
## [46] "Q12 RN5" "Q13 RN6" "Q14 RN7"
## [49] "Q15 LP1" "Q16 LP2" "Q17 LP3"
## [52] "Q18 LP4" "Q19 LP5" "Q20 LP6"
## [55] "Q21 LP7" "Q22 LN1" "Q23 LN2"
## [58] "Q24 LN3" "Q25 LN4" "Q26 LN5"
## [61] "Q27 LN6" "Q28 LN7" "spwtime_First Click"
## [64] "spwtime_Last Click" "spwtime_Page Submit" "spwtime_Click Count"
## [67] "attentioncheck_nb" "discrepancy_nb" "BMIS_sad_2"
## [70] "BMIS_shame_2" "BMIS_guilt_2" "BMIS_tired_2"
## [73] "BMIS_nervous_2" "BMIS_calm_2" "BMIS_fedup_2"
## [76] "BMIS_loving_2" "BMIS_angry_2" "BMIS_lively_2"
## [79] "BMIS_caring_2" "BMIS_content_2" "BMIS_gloomy_2"
## [82] "BMIS_jittery_2" "BMIS_drowsy_2" "BMIS_happy_2"
## [85] "credibility" "objective" "valid"
## [88] "useful" "rl_product_choice" "rl_shop_intentions"
## [91] "rl_purchase" "rl_wom" "rr_product_choice"
## [94] "rr_shop_intentions" "rr_purchase" "rr_wom"
## [97] "attentioncheck_b" "discrepancy_b" "SE_2"
## [100] "age" "race" "education"
## [103] "polit_affil" "polit_affil_4_TEXT" "polit_affil_cont_1"
## [106] "gender" "gender_4_TEXT" "iat_prev"
## [109] "iat_racial" "iat_racial_time" "iat_racial_quant"
## [112] "recent_results" "nobias_white" "nobias_black"
## [115] "bias_white" "bias_black"summary(wf_dt[,19:34])## BMIS_sad_1 BMIS_shame_1 BMIS_guilt_1 BMIS_tired_1 BMIS_nervous_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :1.000 Median :3.00 Median :2.000
## Mean :1.923 Mean :1.611 Mean :1.675 Mean :2.71 Mean :1.871
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.00 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000
## NA's :16 NA's :16 NA's :16 NA's :17 NA's :16
## BMIS_calm_1 BMIS_fedup_1 BMIS_loving_1 BMIS_angry_1 BMIS_lively_1
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.00 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :1.00 Median :3.000 Median :1.000 Median :2.000
## Mean :3.132 Mean :1.81 Mean :2.728 Mean :1.437 Mean :2.259
## 3rd Qu.:4.000 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :16 NA's :16 NA's :18 NA's :16 NA's :18
## BMIS_caring_1 BMIS_content_1 BMIS_gloomy_1 BMIS_jittery_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :2.000 Median :1.000
## Mean :2.929 Mean :2.842 Mean :1.897 Mean :1.605
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :16 NA's :16 NA's :16 NA's :16
## BMIS_drowsy_1 BMIS_happy_1
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000
## Mean :2.129 Mean :2.768
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000
## NA's :17 NA's :16summary(wf_dt[,69:84])## BMIS_sad_2 BMIS_shame_2 BMIS_guilt_2 BMIS_tired_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :1.000 Median :3.000
## Mean :1.986 Mean :1.776 Mean :1.763 Mean :2.642
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :32 NA's :33 NA's :32 NA's :34
## BMIS_nervous_2 BMIS_calm_2 BMIS_fedup_2 BMIS_loving_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :1.000 Median :3.000
## Mean :1.844 Mean :2.963 Mean :1.687 Mean :2.693
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :33 NA's :32 NA's :33 NA's :34
## BMIS_angry_2 BMIS_lively_2 BMIS_caring_2 BMIS_content_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :1.000 Median :2.000 Median :3.000 Median :3.000
## Mean :1.485 Mean :2.278 Mean :2.827 Mean :2.803
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.500
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :32 NA's :32 NA's :33 NA's :32
## BMIS_gloomy_2 BMIS_jittery_2 BMIS_drowsy_2 BMIS_happy_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :2.000 Median :3.000
## Mean :1.898 Mean :1.646 Mean :2.156 Mean :2.617
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :33 NA's :33 NA's :32 NA's :32summary(wf_dt[,14:17])## BA_1 BA_2 BA_3 BA_4
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :4.00 Median :4.000
## Mean :3.334 Mean :3.273 Mean :3.73 Mean :3.987
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.500
## Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000
## NA's :16 NA's :16 NA's :16 NA's :16wf_dt$discrepancy_nb <- car::recode(wf_dt$discrepancy_nb, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$attentioncheck_nb <- car::recode(wf_dt$attentioncheck_nb, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$discrepancy_b <- car::recode(wf_dt$discrepancy_b, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$attentioncheck_b <- car::recode(wf_dt$attentioncheck_b, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'") # larger scores indicate greater changes
wf_dt$discrepancy_bias <- (wf_dt$discrepancy_b - wf_dt$attentioncheck_b) # deal with this later
wf_dt$discrepancy_nobias <- (wf_dt$discrepancy_nb + wf_dt$discrepancy_nb) # deal with this later
wf_dt$bias_discrepancy <- coalesce(wf_dt$discrepancy_bias, wf_dt$discrepancy_nobias) wf_dt$attentioncheck_nb <- as.numeric(wf_dt$attentioncheck_nb)
is.numeric(wf_dt$attentioncheck_nb)## [1] TRUEwhich(wf_dt$attentioncheck_nb == "1", arr.ind = TRUE) # seeing which people failed my attention checks## [1] 36 116 179 212 277 283 310 318 319which(wf_dt$attentioncheck_nb == "2", arr.ind = TRUE)## [1] 77 115 208 240 247 249which(wf_dt$attentioncheck_nb == "3", arr.ind = TRUE)## [1] 21 167 192 325which(wf_dt$attentioncheck_nb == "5", arr.ind = TRUE)## integer(0)which(wf_dt$attentioncheck_nb == "6", arr.ind = TRUE)## integer(0)which(wf_dt$attentioncheck_nb == "7", arr.ind = TRUE)## integer(0)wf_dt$attentioncheck_b <- as.numeric(wf_dt$attentioncheck_b)
is.numeric(wf_dt$attentioncheck_b)## [1] TRUEwhich(wf_dt$attentioncheck_b == "4", arr.ind = TRUE) # seeing which people failed my attention checks## integer(0)which(wf_dt$attentioncheck_b == "2", arr.ind = TRUE)## [1] 2 26 69 134 200 201 251 261 272which(wf_dt$attentioncheck_b == "3", arr.ind = TRUE)## [1] 5 9 11 17 20 24 29 30 34 35 37 38 40 43 44 45 48 49
## [19] 50 52 56 58 62 63 64 73 75 80 83 86 87 91 92 93 94 97
## [37] 98 102 104 105 107 108 112 122 124 125 128 129 131 135 136 137 139 140
## [55] 143 144 146 147 149 150 151 154 157 160 161 163 164 176 180 182 183 184
## [73] 185 186 187 188 189 190 193 195 196 198 202 205 226 231 232 233 236 238
## [91] 239 245 248 252 253 255 257 258 259 263 266 268 270 273 275 276 278 279
## [109] 281 282 285 286 289 293 294 296 297 300 304 305 306 311 312 313 316 317
## [127] 320 321 322 324 326 327which(wf_dt$attentioncheck_b == "5", arr.ind = TRUE)## integer(0)which(wf_dt$attentioncheck_b == "6", arr.ind = TRUE)## integer(0)which(wf_dt$attentioncheck_b == "7", arr.ind = TRUE)## integer(0)wf_dt2 <- wf_dt[-c(61, 81, 88, 82, 15, 301, 315, 323, 254),] # only lost 9 peoplewf_dt2$bias_discrepancy_centered <- scale(wf_dt2$bias_discrepancy, center = TRUE, scale = FALSE) # Centering
wf_dt2$bias_discrepancy_Z <- scale(wf_dt2$bias_discrepancy, center = TRUE, scale = TRUE) # Z-scorewf_dt2 <- wf_dt2 %>%
mutate(
condition = rowSums(select(., nobias_white, nobias_black, bias_white, bias_black), na.rm = TRUE)
)
as.factor(wf_dt2$condition)## [1] 0 3 0 1 2 0 1 0 2 1 3 1 0 1 0 3 1 0 2 1 1 0 3 1 2 0 1 3 3 0 1 3 2 3 0 3 2
## [38] 0 3 1 0 2 2 3 0 0 2 2 2 0 3 0 1 1 3 1 3 3 0 2 3 2 0 1 0 1 2 0 2 0 2 0 3 1
## [75] 1 3 1 2 2 0 0 2 3 2 0 2 3 3 3 1 1 3 2 1 1 0 3 0 3 3 1 3 3 0 0 1 3 0 0 1 1
## [112] 0 2 1 0 2 2 0 2 3 0 1 3 3 0 3 1 0 2 3 2 2 1 2 2 1 1 2 2 0 2 3 0 3 2 2 1 0
## [149] 3 0 3 3 1 0 3 2 0 2 2 0 1 0 1 1 0 1 0 0 1 1 3 1 0 0 2 1 3 2 2 2 2 3 2 3 3
## [186] 0 1 3 1 3 2 0 3 0 3 2 2 3 2 3 0 3 1 1 0 0 1 0 0 0 0 3 1 1 2 1 0 0 3 2 2 2
## [223] 1 0 0 2 3 2 0 1 3 1 3 2 0 0 1 2 0 3 0 1 2 1 0 2 3 3 2 1 3 2 3 0 2 1 3 0 1
## [260] 3 0 2 1 2 0 3 2 1 3 2 0 2 3 1 2 3 0 1 2 3 1 0 2 1 0 0 2 3 0 3 2 0 1 2 0 0
## [297] 2 3 3 0 1 0 1 3 3 2 1 2 3 1 1 2 2 3 2 1 2 3
## Levels: 0 1 2 3wf_dt2 <- wf_dt2 %>%
mutate(
bias = case_when(
condition %in% c(0, 1) ~ 0,
condition %in% c(2, 3) ~ 1,
TRUE ~ NA_integer_
),
brand_race = case_when(
condition %in% c(0, 2) ~ 0,
condition %in% c(1, 3) ~ 1,
TRUE ~ NA_integer_
)
)wf_dt2 <- wf_dt2 %>%
mutate(
shop_intentions = rowSums(select(., rl_shop_intentions, rr_shop_intentions), na.rm = TRUE)
)
# Centering and z-score for "shop_intentions"
wf_dt2$shop_intentions_centered <- scale(wf_dt2$shop_intentions, center = TRUE, scale = FALSE) # Centering
wf_dt2$shop_intentions_Z <- scale(wf_dt2$shop_intentions, center = TRUE, scale = TRUE) # Z-scorewf_dt2 <- wf_dt2 %>%
mutate(
purchase = rowSums(select(., rl_purchase, rr_purchase), na.rm = TRUE)
)
# Centering and z-score for "purchase"
wf_dt2$purchase_centered <- scale(wf_dt2$purchase, center = TRUE, scale = FALSE) # Centering
wf_dt2$purchase_Z <- scale(wf_dt2$purchase, center = TRUE, scale = TRUE) # Z-scorewf_dt2 <- wf_dt2 %>%
mutate(
wom = rowSums(select(., rl_wom, rr_wom), na.rm = TRUE)
)
# Centering and z-score for "wom"
wf_dt2$wom_centered <- scale(wf_dt2$wom, center = TRUE, scale = FALSE) # Centering
wf_dt2$wom_Z <- scale(wf_dt2$wom, center = TRUE, scale = TRUE) # Z-score** Demographics**
race_proportions <- table(wf_dt2$race)/length(wf_dt2$race) # creating race table
(race_percentages <- race_proportions*100) # multiplying the table by 100 to get percentages##
## 1 3 4 6 7 14 15
## 86.7924528 0.3144654 0.3144654 0.3144654 0.6289308 0.3144654 0.3144654
## 16
## 0.9433962gender_proportions <- table(wf_dt2$gender)/length(wf_dt2$gender) # creating gender table
(gender_percentages <- gender_proportions*100) # multiplying the table by 100 to get percentages##
## 1 2 3 4 5
## 41.8238994 45.2830189 2.2012579 0.3144654 0.3144654political_proportions <- table(wf_dt2$polit_affil)/length(wf_dt2$polit_affil) # creating political table
(political_percentages <- political_proportions*100) # multiplying the table by 100 to get percentages##
## 1 2 3 4 5
## 17.610063 47.169811 22.012579 1.886792 1.257862education_proportions <- table(wf_dt2$education)/length(wf_dt2$education) # creating education table
(education_percentages <- education_proportions*100) # multiplying the table by 100 to get percentages##
## 1 2 3 4 5 6 7
## 0.6289308 10.6918239 18.8679245 9.7484277 36.4779874 12.8930818 0.6289308iatprev_proportions <- table(wf_dt2$iat_prev)/length(wf_dt2$iat_prev) # creating IAT previously table
(iatprev_percentages <- iatprev_proportions*100) # multiplying the table by 100 to get percentages##
## 21 22
## 59.74843 30.18868iatracial_proportions <- table(wf_dt2$iat_racial)/length(wf_dt2$iat_racial) # creating IAT racial table
(iatracial_percentages <- iatracial_proportions*100) # multiplying the table by 100 to get percentages##
## 21 22
## 6.289308 23.8993711. Defensiveness
hildebrand.rule(credibility, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.115851 No Skewhildebrand.rule(objective, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.008063028 No Skewhildebrand.rule(valid, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1385317 No Skewhildebrand.rule(useful, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.2647419 Negative Skewsd(credibility, na.rm = TRUE)## [1] 1.708679sd(objective, na.rm = TRUE)## [1] 1.698944sd(valid, na.rm = TRUE)## [1] 1.705757sd(useful, na.rm = TRUE)## [1] 1.759273hist(credibility)
hist(objective)
hist(valid)
hist(useful)
defensive <- select(wf_dt2, 85:88)
psych::alpha(defensive)##
## Reliability analysis
## Call: psych::alpha(x = defensive)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.91 0.74 11 0.0078 3.8 1.6 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.92 0.93
## Duhachek 0.9 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## credibility 0.87 0.87 0.84 0.70 6.9 0.0126 0.0144 0.67
## objective 0.94 0.94 0.91 0.83 14.7 0.0063 0.0022 0.83
## valid 0.86 0.86 0.82 0.68 6.3 0.0135 0.0094 0.65
## useful 0.89 0.89 0.87 0.73 8.3 0.0109 0.0155 0.67
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## credibility 284 0.93 0.93 0.91 0.86 3.8 1.7
## objective 283 0.81 0.82 0.70 0.68 4.0 1.7
## valid 283 0.94 0.94 0.94 0.90 3.8 1.7
## useful 283 0.90 0.90 0.85 0.81 3.5 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## credibility 0.15 0.11 0.10 0.27 0.23 0.08 0.06 0.11
## objective 0.13 0.06 0.12 0.30 0.17 0.14 0.07 0.11
## valid 0.15 0.10 0.17 0.23 0.19 0.11 0.05 0.11
## useful 0.18 0.15 0.15 0.21 0.19 0.07 0.06 0.11wf_dt2$defensive <- rowMeans(wf_dt2[,85:88]) # creating defensiveness variable
myscale <- 1:7 #defining scale to reverse defensivness variable
wf_dt2 <- wf_dt2 %>%
mutate(defensive_reverse = min(myscale) - defensive + max(myscale)) # reversing defensivness scale
wf_dt2$defensive_reverse_centered <- scale(wf_dt2$defensive_reverse, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$defensive_reverse_Z <- scale(wf_dt2$defensive_reverse, center = TRUE, scale = TRUE) # z score defensiveness (def_r_mean = mean(wf_dt2$defensive_reverse, na.rm = TRUE)) # mean defensivness## [1] 4.241906(def_r_sd = sd(wf_dt2$defensive_reverse, na.rm = TRUE)) # sd defensiveness## [1] 1.548895hist(wf_dt2$defensive_reverse_Z) # histogram of defensive z scores
# Perform ANOVA for defensive_reverse by bias
summary((anova_defensive_reverse <- aov(defensive_reverse ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(bias) 1 56.5 56.49 25.670 0.000000746
## as.factor(brand_race) 1 0.5 0.53 0.243 0.622
## as.factor(bias):as.factor(brand_race) 1 4.5 4.50 2.047 0.154
## Residuals 274 603.0 2.20
##
## as.factor(bias) ***
## as.factor(brand_race)
## as.factor(bias):as.factor(brand_race)
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 40 observations deleted due to missingness# Get summary of ANOVA
(tukey_posthoc <- TukeyHSD(anova_defensive_reverse))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = defensive_reverse ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
##
## $`as.factor(bias)`
## diff lwr upr p adj
## 1-0 0.9044271 0.5530046 1.25585 0.0000007
##
## $`as.factor(brand_race)`
## diff lwr upr p adj
## 1-0 0.08766191 -0.2626675 0.4379914 0.6226813
##
## $`as.factor(bias):as.factor(brand_race)`
## diff lwr upr p adj
## 1:0-0:0 0.6523987 -0.00004964437 1.3048470 0.0500256
## 0:1-0:0 -0.1879277 -0.86613489865 0.4902796 0.8905901
## 1:1-0:0 0.9755824 0.31120396399 1.6399609 0.0010360
## 0:1-1:0 -0.8403263 -1.48166470937 -0.1989880 0.0044803
## 1:1-1:0 0.3231838 -0.30351282130 0.9498803 0.5427063
## 1:1-0:1 1.1635101 0.51003871844 1.8169815 0.00003782. Bias Awareness
hildebrand.rule(BA_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1849701 No Skewhildebrand.rule(BA_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1529581 No Skewhildebrand.rule(BA_3, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.148806 No Skewhildebrand.rule(BA_4, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.006890947 No Skewsd(BA_1, na.rm = TRUE)## [1] 1.807888sd(BA_2, na.rm = TRUE)## [1] 1.786841sd(BA_3, na.rm = TRUE)## [1] 1.815091sd(BA_4, na.rm = TRUE)## [1] 1.866469hist(BA_1)
hist(BA_2)
hist(BA_3)
hist(BA_4)
bias_aware <- select(wf_dt2, 14:17)
psych::alpha(bias_aware)##
## Reliability analysis
## Call: psych::alpha(x = bias_aware)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.79 0.53 4.5 0.017 3.6 1.5 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.82 0.85
## Duhachek 0.78 0.82 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## BA_1 0.81 0.81 0.75 0.59 4.3 0.019 0.0084 0.55
## BA_2 0.75 0.75 0.69 0.50 3.0 0.024 0.0123 0.55
## BA_3 0.71 0.71 0.63 0.45 2.5 0.028 0.0053 0.46
## BA_4 0.80 0.80 0.75 0.58 4.1 0.019 0.0141 0.58
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## BA_1 302 0.75 0.75 0.61 0.55 3.3 1.8
## BA_2 302 0.83 0.83 0.76 0.68 3.3 1.8
## BA_3 302 0.88 0.88 0.85 0.76 3.7 1.8
## BA_4 302 0.77 0.76 0.63 0.57 4.0 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## BA_1 0.19 0.25 0.13 0.06 0.24 0.11 0.02 0.05
## BA_2 0.20 0.26 0.13 0.06 0.25 0.08 0.03 0.05
## BA_3 0.15 0.19 0.12 0.09 0.30 0.12 0.04 0.05
## BA_4 0.12 0.18 0.12 0.09 0.25 0.18 0.07 0.05wf_dt2$bias_aware <- rowMeans(wf_dt2[,14:17])
wf_dt2$bias_aware_center <- scale(wf_dt2$bias_aware, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$bias_aware_Z <- scale(wf_dt2$bias_aware, center = TRUE, scale = TRUE) #Z scores (BA_mean = mean(wf_dt2$bias_aware, na.rm = TRUE)) # mean of bias awareness## [1] 3.563742(BA_sd = sd(wf_dt2$bias_aware, na.rm = TRUE)) # sd of bias awareness## [1] 1.463663hist(wf_dt2$bias_aware_Z)
# Perform ANOVA for bias_aware by condition
summary((anova_bias_aware <- aov(bias_aware ~ as.factor(condition), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(condition) 3 5.1 1.714 0.798 0.496
## Residuals 298 639.7 2.147
## 16 observations deleted due to missingness# Run Tukey post hoc test
(tukey_posthoc <- TukeyHSD(anova_bias_aware))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = bias_aware ~ as.factor(condition), data = wf_dt2)
##
## $`as.factor(condition)`
## diff lwr upr p adj
## 1-0 -0.29681467 -0.9279424 0.3343131 0.6177784
## 2-0 0.01830357 -0.6012124 0.6378196 0.9998402
## 3-0 -0.18177656 -0.8049880 0.4414349 0.8750921
## 2-1 0.31511824 -0.2954025 0.9256390 0.5423101
## 3-1 0.11503812 -0.4992322 0.7293085 0.9626422
## 3-2 -0.20008013 -0.8024138 0.4022536 0.82631523. Emotions
hildebrand.rule(BMIS_lively_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.290114 Positive Skewhildebrand.rule(BMIS_lively_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.3121008 Positive Skewhildebrand.rule(BMIS_happy_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.2571164 Negative Skewhildebrand.rule(BMIS_happy_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.4123413 Negative Skewhildebrand.rule(BMIS_sad_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.0779093 No Skewhildebrand.rule(BMIS_sad_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.01324874 No Skewhildebrand.rule(BMIS_shame_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7093866 Positive Skewhildebrand.rule(BMIS_shame_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.803145 Positive Skewhildebrand.rule(BMIS_guilt_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7735714 Positive Skewhildebrand.rule(BMIS_guilt_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7938481 Positive Skewhildebrand.rule(BMIS_tired_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.299749 Negative Skewhildebrand.rule(BMIS_tired_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.3492858 Negative Skewhildebrand.rule(BMIS_caring_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.07747474 No Skewhildebrand.rule(BMIS_caring_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1813282 No Skewhildebrand.rule(BMIS_content_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1684255 No Skewhildebrand.rule(BMIS_content_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.2072466 Negative Skewhildebrand.rule(BMIS_gloomy_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1031091 No Skewhildebrand.rule(BMIS_gloomy_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.09969695 No Skewhildebrand.rule(BMIS_jittery_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7373568 Positive Skewhildebrand.rule(BMIS_jittery_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7611705 Positive Skewhildebrand.rule(BMIS_drowsy_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1266293 No Skewhildebrand.rule(BMIS_drowsy_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1536428 No Skewhildebrand.rule(BMIS_nervous_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1351261 No Skewhildebrand.rule(BMIS_nervous_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.1633531 No Skewhildebrand.rule(BMIS_calm_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.1559281 No Skewhildebrand.rule(BMIS_calm_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.04192267 No Skewhildebrand.rule(BMIS_jittery_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7373568 Positive Skewhildebrand.rule(BMIS_jittery_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7611705 Positive Skewhildebrand.rule(BMIS_loving_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.2786378 Negative Skewhildebrand.rule(BMIS_loving_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx -0.3080362 Negative Skewhildebrand.rule(BMIS_fedup_1, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.8267106 Positive Skewhildebrand.rule(BMIS_fedup_2, na.rm = TRUE)## Group Ratio Skew
## 1 xx 0.7907416 Positive Skewsd(BMIS_lively_1, na.rm = TRUE)## [1] 0.8924067sd(BMIS_lively_2, na.rm = TRUE)## [1] 0.8906292sd(BMIS_happy_1, na.rm = TRUE)## [1] 0.9004142sd(BMIS_happy_2, na.rm = TRUE)## [1] 0.9289656sd(BMIS_sad_1, na.rm = TRUE)## [1] 0.9905162sd(BMIS_sad_2, na.rm = TRUE)## [1] 1.023443sd(BMIS_shame_1, na.rm = TRUE)## [1] 0.8612123sd(BMIS_shame_2, na.rm = TRUE)## [1] 0.9655918sd(BMIS_guilt_1, na.rm = TRUE)## [1] 0.872888sd(BMIS_guilt_2, na.rm = TRUE)## [1] 0.9607782sd(BMIS_tired_1, na.rm = TRUE)## [1] 0.9685524sd(BMIS_tired_2, na.rm = TRUE)## [1] 1.025984sd(BMIS_caring_1, na.rm = TRUE)## [1] 0.913066sd(BMIS_caring_2, na.rm = TRUE)## [1] 0.9566599sd(BMIS_content_1, na.rm = TRUE)## [1] 0.9354656sd(BMIS_content_2, na.rm = TRUE)## [1] 0.9486772sd(BMIS_gloomy_1, na.rm = TRUE)## [1] 0.997913sd(BMIS_gloomy_2, na.rm = TRUE)## [1] 1.02351sd(BMIS_jittery_1, na.rm = TRUE)## [1] 0.8198224sd(BMIS_jittery_2, na.rm = TRUE)## [1] 0.8490325sd(BMIS_drowsy_1, na.rm = TRUE)## [1] 1.018976sd(BMIS_drowsy_2, na.rm = TRUE)## [1] 1.014901sd(BMIS_nervous_1, na.rm = TRUE)## [1] 0.9518324sd(BMIS_nervous_2, na.rm = TRUE)## [1] 0.9578181sd(BMIS_calm_1, na.rm = TRUE)## [1] 0.8454715sd(BMIS_calm_2, na.rm = TRUE)## [1] 0.8894503sd(BMIS_jittery_1, na.rm = TRUE)## [1] 0.8198224sd(BMIS_jittery_2, na.rm = TRUE)## [1] 0.8490325sd(BMIS_loving_1, na.rm = TRUE)## [1] 0.9756201sd(BMIS_loving_2, na.rm = TRUE)## [1] 0.9971792sd(BMIS_fedup_1, na.rm = TRUE)## [1] 0.9801367sd(BMIS_fedup_2, na.rm = TRUE)## [1] 0.8688993hist(BMIS_lively_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_lively_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_happy_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_happy_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_sad_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_sad_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_shame_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_shame_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_guilt_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_guilt_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_tired_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_tired_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_caring_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_caring_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_content_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_content_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_gloomy_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_gloomy_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_jittery_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_jittery_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_drowsy_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_drowsy_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_nervous_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_nervous_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_calm_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_calm_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_jittery_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_jittery_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_loving_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_loving_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_fedup_1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(BMIS_fedup_2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
wf_dt2$BMIS_guilt_1_center <- scale(wf_dt2$BMIS_guilt_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_guilt_1_Z <- scale(wf_dt2$BMIS_guilt_1, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_shame_1_center <- scale(wf_dt2$BMIS_shame_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_shame_1_Z <- scale(wf_dt2$BMIS_shame_1, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_sad_1_center <- scale(wf_dt2$BMIS_sad_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_sad_1_Z <- scale(wf_dt2$BMIS_sad_1, center = TRUE, scale = TRUE) #Z scorewf_dt2$BMIS_guilt_2_center <- scale(wf_dt2$BMIS_guilt_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_guilt_2_Z <- scale(wf_dt2$BMIS_guilt_2, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_shame_2_center <- scale(wf_dt2$BMIS_shame_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_shame_2_Z <- scale(wf_dt2$BMIS_shame_2, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_sad_2_center <- scale(wf_dt2$BMIS_sad_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_sad_2_Z <- scale(wf_dt2$BMIS_sad_2, center = TRUE, scale = TRUE) #Z score(guilty_mean = mean(wf_dt2$BMIS_guilt, na.rm = TRUE))## Warning: Unknown or uninitialised column: `BMIS_guilt`.## Warning in mean.default(wf_dt2$BMIS_guilt, na.rm = TRUE): argument is not
## numeric or logical: returning NA## [1] NA(guilty_sd = sd(wf_dt2$BMIS_guilt, na.rm = TRUE))## Warning: Unknown or uninitialised column: `BMIS_guilt`.## [1] NA(shame_mean = mean(wf_dt2$BMIS_shame, na.rm = TRUE))## Warning: Unknown or uninitialised column: `BMIS_shame`.## Warning in mean.default(wf_dt2$BMIS_shame, na.rm = TRUE): argument is not
## numeric or logical: returning NA## [1] NA(shame_sd = sd(wf_dt2$BMIS_shame, na.rm = TRUE))## Warning: Unknown or uninitialised column: `BMIS_shame`.## [1] NAguilt_shame_sad <- select(wf_dt2, BMIS_sad_2, BMIS_shame_2, BMIS_guilt_2) # dropping certain variables for the alpha
psych::alpha(guilt_shame_sad)##
## Reliability analysis
## Call: psych::alpha(x = guilt_shame_sad)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.9 0.76 9.5 0.01 1.8 0.9 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.92
## Duhachek 0.88 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## BMIS_sad_2 0.97 0.97 0.94 0.94 29.3 0.0037 NA 0.94
## BMIS_shame_2 0.80 0.80 0.66 0.66 4.0 0.0226 NA 0.66
## BMIS_guilt_2 0.81 0.81 0.68 0.68 4.2 0.0216 NA 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## BMIS_sad_2 286 0.86 0.85 0.69 0.68 2.0 1.02
## BMIS_shame_2 285 0.95 0.95 0.96 0.88 1.8 0.96
## BMIS_guilt_2 286 0.94 0.95 0.95 0.87 1.8 0.96
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## BMIS_sad_2 0.44 0.22 0.25 0.09 0.1
## BMIS_shame_2 0.54 0.20 0.20 0.06 0.1
## BMIS_guilt_2 0.55 0.20 0.20 0.06 0.1wf_dt2$guilt_shame_sad <- rowMeans(guilt_shame_sad)
wf_dt2$guilt_shame_sad_center <- scale(wf_dt2$guilt_shame_sad, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$guilt_shame_sad_Z <- scale(wf_dt2$guilt_shame_sad, center = TRUE, scale = TRUE) #Z scores wf_dt2 <- wf_dt2 %>% mutate(shame_discrepancy = BMIS_shame_2_Z - BMIS_shame_1_Z) # higher scores indicate an increase in shame, lower scores indicate decrease in shame# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(BMIS_shame_2 ~ bias, var.equal = TRUE, data = wf_dt2))##
## Two Sample t-test
##
## data: BMIS_shame_2 by bias
## t = -4.6422, df = 283, p-value = 0.000005282
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.7306252 -0.2955185
## sample estimates:
## mean in group 0 mean in group 1
## 1.500000 2.013072# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(BMIS_shame_2 ~ bias, data = wf_dt2))## [1] 0.5514569# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(BMIS_shame_2 ~ bias, data = wf_dt2, FUN = sd))## bias BMIS_shame_2
## 1 0 0.7666722
## 2 1 1.0512332# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(BMIS_shame_1 ~ bias, var.equal = TRUE, data = wf_dt2))##
## Two Sample t-test
##
## data: BMIS_shame_1 by bias
## t = 0.23429, df = 300, p-value = 0.8149
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.1710664 0.2173041
## sample estimates:
## mean in group 0 mean in group 1
## 1.618056 1.594937# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(BMIS_shame_1 ~ bias, data = wf_dt2))## [1] 0.02699276# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(BMIS_shame_1 ~ bias, data = wf_dt2, FUN = sd))## bias BMIS_shame_1
## 1 0 0.8609356
## 2 1 0.8524074# One-way ANOVA for SE_2 by bias
summary((anova_SE2 <- aov(SE_2 ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(bias) 1 1.5 1.4913 0.850 0.357
## as.factor(brand_race) 1 1.9 1.9410 1.107 0.294
## as.factor(bias):as.factor(brand_race) 1 0.5 0.5037 0.287 0.592
## Residuals 282 494.7 1.7541
## 32 observations deleted due to missingness# Tukey post hoc test
(tukey_SE2 <- TukeyHSD(anova_SE2))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SE_2 ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
##
## $`as.factor(bias)`
## diff lwr upr p adj
## 1-0 -0.1447737 -0.453845 0.1642976 0.357299
##
## $`as.factor(brand_race)`
## diff lwr upr p adj
## 1-0 0.1646979 -0.1436244 0.4730201 0.2939405
##
## $`as.factor(bias):as.factor(brand_race)`
## diff lwr upr p adj
## 1:0-0:0 -0.22516026 -0.8024587 0.3521382 0.7448843
## 0:1-0:0 0.07472826 -0.5192969 0.6687534 0.9881077
## 1:1-0:0 0.01791667 -0.5645624 0.6003957 0.9998195
## 0:1-1:0 0.29988852 -0.2658039 0.8655809 0.5192046
## 1:1-1:0 0.24307692 -0.3104787 0.7966326 0.6682772
## 1:1-0:1 -0.05681159 -0.6277899 0.5141667 0.9940277# One-way ANOVA for SE_2 by bias
summary((anova_SE2 <- aov(SE_2 ~ as.factor(condition), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(condition) 3 3.9 1.312 0.748 0.524
## Residuals 282 494.7 1.754
## 32 observations deleted due to missingness# Tukey post hoc test
(tukey_SE2 <- TukeyHSD(anova_SE2))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SE_2 ~ as.factor(condition), data = wf_dt2)
##
## $`as.factor(condition)`
## diff lwr upr p adj
## 1-0 0.07472826 -0.5192969 0.6687534 0.9881077
## 2-0 -0.22516026 -0.8024587 0.3521382 0.7448843
## 3-0 0.01791667 -0.5645624 0.6003957 0.9998195
## 2-1 -0.29988852 -0.8655809 0.2658039 0.5192046
## 3-1 -0.05681159 -0.6277899 0.5141667 0.9940277
## 3-2 0.24307692 -0.3104787 0.7966326 0.6682772# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(SE_2 ~ bias, var.equal = TRUE, data = wf_dt2))##
## Two Sample t-test
##
## data: SE_2 by bias
## t = 0.92302, df = 284, p-value = 0.3568
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.1639581 0.4535055
## sample estimates:
## mean in group 0 mean in group 1
## 3.007519 2.862745# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(SE_2 ~ bias, data = wf_dt2))## [1] 0.1094265# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(SE_2 ~ bias, data = wf_dt2, FUN = sd))## bias SE_2
## 1 0 1.264289
## 2 1 1.371989wf_dt2$SE_2_Z <- scale(wf_dt2$SE_2, center = TRUE, scale = TRUE) #Z scores
wf_dt2$SE_1_Z <- scale(wf_dt2$SE1, center = TRUE, scale = TRUE) #Z scores
wf_dt2 <- wf_dt2 %>% mutate(SE_change = SE_2_Z - SE_1_Z) # higher scores indicate increases while negative score indicate decreases4. Model Regressions
# One-way ANOVA for purchase by interaction
summary((anova_purchase <- aov(purchase ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(bias) 1 32.9 32.91 8.053 0.00484 **
## as.factor(brand_race) 1 81.7 81.67 19.985 0.0000109 ***
## as.factor(bias):as.factor(brand_race) 1 18.4 18.44 4.511 0.03446 *
## Residuals 314 1283.2 4.09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Tukey post hoc test
(tukey_purchase <- TukeyHSD(anova_purchase))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = purchase ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
##
## $`as.factor(bias)`
## diff lwr upr p adj
## 1-0 0.6434335 0.1973273 1.08954 0.0048373
##
## $`as.factor(brand_race)`
## diff lwr upr p adj
## 1-0 1.014068 0.567538 1.460599 0.000011
##
## $`as.factor(bias):as.factor(brand_race)`
## diff lwr upr p adj
## 1:0-0:0 1.0729651 0.2619190 1.8840112 0.0039778
## 0:1-0:0 1.4956003 0.6676950 2.3235055 0.0000270
## 1:1-0:0 1.6040549 0.7876396 2.4204701 0.0000040
## 0:1-1:0 0.4226351 -0.4195067 1.2647769 0.5660145
## 1:1-1:0 0.5310897 -0.2997590 1.3619385 0.3516638
## 1:1-0:1 0.1084546 -0.7388593 0.9557685 0.98751595. Shame Regressions
summary(lm(BMIS_shame_2_Z ~ bias_aware_Z*as.factor(bias), wf_dt2)) #bias aware sig on its own and interaction##
## Call:
## lm(formula = BMIS_shame_2_Z ~ bias_aware_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8994 -0.5751 -0.4242 0.5885 2.5843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.28392 0.08072 -3.518 0.000508 ***
## bias_aware_Z 0.06689 0.08350 0.801 0.423730
## as.factor(bias)1 0.51996 0.11016 4.720 0.00000372 ***
## bias_aware_Z:as.factor(bias)1 0.29877 0.11051 2.704 0.007279 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9269 on 281 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1499, Adjusted R-squared: 0.1408
## F-statistic: 16.51 on 3 and 281 DF, p-value: 0.0000000006535summary(lm(BMIS_shame_2_Z ~ defensive_reverse_Z*as.factor(bias), wf_dt2)) #defensiveness on its own not sig##
## Call:
## lm(formula = BMIS_shame_2_Z ~ defensive_reverse_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4913 -0.7599 -0.3942 0.7591 2.5916
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.24669 0.08990 -2.744 0.00647 **
## defensive_reverse_Z 0.11327 0.09187 1.233 0.21865
## as.factor(bias)1 0.55590 0.12125 4.585 0.00000693 ***
## defensive_reverse_Z:as.factor(bias)1 -0.29353 0.12194 -2.407 0.01674 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9609 on 273 degrees of freedom
## (41 observations deleted due to missingness)
## Multiple R-squared: 0.09452, Adjusted R-squared: 0.08457
## F-statistic: 9.499 on 3 and 273 DF, p-value: 0.000005463summary(lm(BMIS_shame_2_Z ~ BMIS_shame_1_Z, wf_dt2)) # t1 and t2 are somewhat related##
## Call:
## lm(formula = BMIS_shame_2_Z ~ BMIS_shame_1_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9437 -0.3044 -0.3044 0.1321 2.8093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.003743 0.042316 -0.088 0.93
## BMIS_shame_1_Z 0.700904 0.042380 16.538 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7144 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.4915, Adjusted R-squared: 0.4897
## F-statistic: 273.5 on 1 and 283 DF, p-value: < 0.00000000000000022summary(lm(BMIS_shame_2_Z ~ bias_discrepancy_Z*bias_aware_Z, wf_dt2)) # bias discrepancy is what predicts shame, it is sig when bias condition is in the model, but they do not interact (close at .24 with a postive ineraction). Does interact with bias aware such that those who were more bias aware reported a larger discrepancy and ultimatley reported more shame##
## Call:
## lm(formula = BMIS_shame_2_Z ~ bias_discrepancy_Z * bias_aware_Z,
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6095 -0.6912 -0.3006 0.6511 2.6177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.004705 0.055763 0.084 0.933
## bias_discrepancy_Z -0.257326 0.056514 -4.553 0.00000788 ***
## bias_aware_Z 0.267658 0.055618 4.812 0.00000244 ***
## bias_discrepancy_Z:bias_aware_Z -0.054683 0.060351 -0.906 0.366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9358 on 281 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1335, Adjusted R-squared: 0.1242
## F-statistic: 14.43 on 3 and 281 DF, p-value: 0.000000009085summary(lm(shame_discrepancy ~ bias_discrepancy_Z, wf_dt2)) # larger bias discrepancy was associated with a greater change in shame, does not interact with as.factor bias##
## Call:
## lm(formula = shame_discrepancy ~ bias_discrepancy_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.37177 -0.29593 -0.03296 0.21531 2.83252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.005542 0.043755 -0.127 0.899
## bias_discrepancy_Z -0.232661 0.043760 -5.317 0.000000215 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7387 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.09082, Adjusted R-squared: 0.0876
## F-statistic: 28.27 on 1 and 283 DF, p-value: 0.0000002146summary(lm(shame_discrepancy ~ bias_aware_Z*as.factor(bias), wf_dt2)) # bias awareness by itself non sig, but interacts with bias condition to predict greater increases in shame ##
## Call:
## lm(formula = shame_discrepancy ~ bias_aware_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.33552 -0.38817 0.00329 0.25513 2.55957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.29818 0.06271 -4.755 0.00000317436 ***
## bias_aware_Z -0.11342 0.06487 -1.748 0.0815 .
## as.factor(bias)1 0.53949 0.08558 6.304 0.00000000112 ***
## bias_aware_Z:as.factor(bias)1 0.22142 0.08586 2.579 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7201 on 281 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1419, Adjusted R-squared: 0.1328
## F-statistic: 15.5 on 3 and 281 DF, p-value: 0.000000002351summary(lm(shame_discrepancy ~ defensive_reverse_Z, wf_dt2)) # defensiveness does not predict changes in shame ##
## Call:
## lm(formula = shame_discrepancy ~ defensive_reverse_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.43388 -0.23859 -0.10711 0.00122 3.10294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00155 0.04668 -0.033 0.974
## defensive_reverse_Z 0.07457 0.04675 1.595 0.112
##
## Residual standard error: 0.7768 on 275 degrees of freedom
## (41 observations deleted due to missingness)
## Multiple R-squared: 0.009166, Adjusted R-squared: 0.005563
## F-statistic: 2.544 on 1 and 275 DF, p-value: 0.1119summary(lm(shame_discrepancy ~ SE_change, wf_dt2)) # defensiveness does not predict changes in shame ##
## Call:
## lm(formula = shame_discrepancy ~ SE_change, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4376 -0.2412 -0.1151 0.1553 3.0150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.003262 0.045435 -0.072 0.9428
## SE_change -0.357664 0.148738 -2.405 0.0168 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7669 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.02002, Adjusted R-squared: 0.01656
## F-statistic: 5.782 on 1 and 283 DF, p-value: 0.016836. Defensiveness Regressions
summary(lm(defensive_reverse_Z ~ bias_aware_Z, wf_dt2)) # bias aware predicts defensiveness on its own but not in an interaction with bias##
## Call:
## lm(formula = defensive_reverse_Z ~ bias_aware_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.34709 -0.70563 -0.05711 0.74082 1.97285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0001677 0.0594346 -0.003 0.9978
## bias_aware_Z -0.1451347 0.0589087 -2.464 0.0144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.991 on 276 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.02152, Adjusted R-squared: 0.01797
## F-statistic: 6.07 on 1 and 276 DF, p-value: 0.01436summary(lm(defensive_reverse_Z ~ bias_discrepancy_Z, wf_dt2)) # bias discrepancy is associated with more defensiveness (sig with bias aware in model)##
## Call:
## lm(formula = defensive_reverse_Z ~ bias_discrepancy_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.49872 -0.72067 -0.07504 0.72938 2.18636
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.005674 0.057373 -0.099 0.921
## bias_discrepancy_Z -0.304138 0.058734 -5.178 0.000000432 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9564 on 276 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.08855, Adjusted R-squared: 0.08525
## F-statistic: 26.81 on 1 and 276 DF, p-value: 0.0000004323summary(lm(SE_2_Z ~ defensive_reverse_Z*as.factor(bias), wf_dt2)) # SE 2 and SE change are related to defensivness until bias condition is included in the model as an interaction term ##
## Call:
## lm(formula = SE_2_Z ~ defensive_reverse_Z * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.65241 -0.83224 0.08357 0.77345 1.84127
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.003339 0.093068 -0.036 0.971
## defensive_reverse_Z -0.122163 0.095120 -1.284 0.200
## as.factor(bias)1 -0.033667 0.125646 -0.268 0.789
## defensive_reverse_Z:as.factor(bias)1 -0.012228 0.126361 -0.097 0.923
##
## Residual standard error: 0.9969 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.01818, Adjusted R-squared: 0.007434
## F-statistic: 1.692 on 3 and 274 DF, p-value: 0.1691summary(lm(defensive_reverse_Z ~ as.factor(brand_race)*as.factor(bias), wf_dt2)) # they obviosuly dont interact##
## Call:
## lm(formula = defensive_reverse_Z ~ as.factor(brand_race) * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.47040 -0.57936 -0.04932 0.64355 2.15452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2525 0.1216 -2.076 0.0388 *
## as.factor(brand_race)1 -0.1213 0.1694 -0.716 0.4744
## as.factor(bias)1 0.4212 0.1630 2.585 0.0103 *
## as.factor(brand_race)1:as.factor(bias)1 0.3300 0.2306 1.431 0.1537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9578 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.0926, Adjusted R-squared: 0.08266
## F-statistic: 9.32 on 3 and 274 DF, p-value: 0.000006898summary(lm(defensive_reverse_Z ~ as.factor(iat_prev), wf_dt2)) # IAt previous does not predict defensiveness##
## Call:
## lm(formula = defensive_reverse_Z ~ as.factor(iat_prev), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1071 -0.7751 -0.1295 0.7982 1.8074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01409 0.07425 0.190 0.850
## as.factor(iat_prev)22 -0.04081 0.12634 -0.323 0.747
##
## Residual standard error: 1.002 on 276 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.0003779, Adjusted R-squared: -0.003244
## F-statistic: 0.1044 on 1 and 276 DF, p-value: 0.7469summary(lm(defensive_reverse_Z ~ as.factor(gender), wf_dt2)) # nothing with gender##
## Call:
## lm(formula = defensive_reverse_Z ~ as.factor(gender), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.17782 -0.70443 -0.05881 0.72748 1.87806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.09737 0.08829 -1.103 0.271
## as.factor(gender)2 0.18215 0.12239 1.488 0.138
## as.factor(gender)3 0.21789 0.38918 0.560 0.576
## as.factor(gender)4 0.26400 1.00670 0.262 0.793
## as.factor(gender)5 -0.22021 1.00670 -0.219 0.827
##
## Residual standard error: 1.003 on 273 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.008879, Adjusted R-squared: -0.005643
## F-statistic: 0.6114 on 4 and 273 DF, p-value: 0.6547summary(lm(defensive_reverse_Z ~ education + bias_aware_Z, wf_dt2)) # education is negativley related to defensiveness, and the effect holds even while bias aware is in the model##
## Call:
## lm(formula = defensive_reverse_Z ~ education + bias_aware_Z,
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.21614 -0.66533 -0.07358 0.65799 1.99089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.50493 0.19941 2.532 0.01189 *
## education -0.11909 0.04493 -2.651 0.00849 **
## bias_aware_Z -0.12196 0.05893 -2.070 0.03942 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9803 on 275 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.0459, Adjusted R-squared: 0.03896
## F-statistic: 6.615 on 2 and 275 DF, p-value: 0.001564summary(lm(defensive_reverse_Z ~ age, wf_dt2)) # nothing with age##
## Call:
## lm(formula = defensive_reverse_Z ~ age, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1370 -0.7582 -0.1160 0.7711 1.8209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.101593 0.197490 -0.514 0.607
## age 0.002274 0.004284 0.531 0.596
##
## Residual standard error: 1.005 on 274 degrees of freedom
## (42 observations deleted due to missingness)
## Multiple R-squared: 0.001027, Adjusted R-squared: -0.002619
## F-statistic: 0.2818 on 1 and 274 DF, p-value: 0.5966. Purchase Intentions
summary(lm(purchase_Z ~ as.factor(bias)*as.factor(brand_race), wf_dt2)) # main interaction working, it is defensiveness driving this effect, whe defensiveness is included as a term in this model the effect vanishes, but defensiveness on its own is still negatively correlated with the dv, it also loses its sig for shame and se, but they are not signifgicant predictors of the dv##
## Call:
## lm(formula = purchase_Z ~ as.factor(bias) * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0077 -0.8102 0.1360 0.8310 2.0630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4785 0.1031 -4.640 0.00000513
## as.factor(bias)1 0.5076 0.1486 3.417 0.000717
## as.factor(brand_race)1 0.7076 0.1516 4.666 0.00000456
## as.factor(bias)1:as.factor(brand_race)1 -0.4563 0.2148 -2.124 0.034459
##
## (Intercept) ***
## as.factor(bias)1 ***
## as.factor(brand_race)1 ***
## as.factor(bias)1:as.factor(brand_race)1 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9564 on 314 degrees of freedom
## Multiple R-squared: 0.09392, Adjusted R-squared: 0.08527
## F-statistic: 10.85 on 3 and 314 DF, p-value: 0.000000842summary(lm(purchase ~ shame_discrepancy*as.factor(bias), wf_dt2)) # shame does not predict it neither does shame change, and it dosent ineract with anything to predict it##
## Call:
## lm(formula = purchase ~ shame_discrepancy * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0120 -1.6944 0.0102 1.0594 3.0237
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.98847 0.17856 22.337
## shame_discrepancy -0.01342 0.28019 -0.048
## as.factor(bias)1 0.04971 0.23526 0.211
## shame_discrepancy:as.factor(bias)1 0.28505 0.33075 0.862
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## shame_discrepancy 0.962
## as.factor(bias)1 0.833
## shame_discrepancy:as.factor(bias)1 0.390
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.819 on 281 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.009381, Adjusted R-squared: -0.001195
## F-statistic: 0.887 on 3 and 281 DF, p-value: 0.4482summary(lm(purchase ~ defensive_reverse_Z*as.factor(brand_race), wf_dt2)) # defensiveness does predict it##
## Call:
## lm(formula = purchase ~ defensive_reverse_Z * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5823 -1.2294 0.2343 1.4127 3.6940
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.6915 0.1490 24.776
## defensive_reverse_Z -0.4747 0.1504 -3.156
## as.factor(brand_race)1 0.7093 0.2115 3.354
## defensive_reverse_Z:as.factor(brand_race)1 0.3493 0.2119 1.649
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## defensive_reverse_Z 0.001776 **
## as.factor(brand_race)1 0.000909 ***
## defensive_reverse_Z:as.factor(brand_race)1 0.100347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.763 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.07302, Adjusted R-squared: 0.06287
## F-statistic: 7.194 on 3 and 274 DF, p-value: 0.0001152summary(lm(purchase ~ SE_change, wf_dt2)) # self-esteem change does not##
## Call:
## lm(formula = purchase ~ SE_change, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0669 -1.7604 -0.0625 0.9375 3.0428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0602 0.1077 37.707 <0.0000000000000002 ***
## SE_change -0.1422 0.3531 -0.403 0.687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.821 on 284 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.0005709, Adjusted R-squared: -0.002948
## F-statistic: 0.1622 on 1 and 284 DF, p-value: 0.6874summary(lm(purchase ~ bias_discrepancy, wf_dt2)) # BD does not##
## Call:
## lm(formula = purchase ~ bias_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.130 -1.684 -0.038 1.123 3.238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.03803 0.11084 36.430 <0.0000000000000002 ***
## bias_discrepancy -0.04604 0.05749 -0.801 0.424
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.819 on 284 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.002253, Adjusted R-squared: -0.00126
## F-statistic: 0.6413 on 1 and 284 DF, p-value: 0.4239summary(lm(purchase ~ bias_aware_Z*as.factor(brand_race)*as.factor(bias), wf_dt2)) # BA does not##
## Call:
## lm(formula = purchase ~ bias_aware_Z * as.factor(brand_race) *
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3864 -1.6991 0.2832 1.6628 3.7582
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 3.243069 0.236294
## bias_aware_Z -0.002706 0.232092
## as.factor(brand_race)1 0.863088 0.330695
## as.factor(bias)1 0.470247 0.323645
## bias_aware_Z:as.factor(brand_race)1 -0.230354 0.346281
## bias_aware_Z:as.factor(bias)1 -0.006271 0.309271
## as.factor(brand_race)1:as.factor(bias)1 -0.337603 0.456278
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.134647 0.462314
## t value
## (Intercept) 13.725
## bias_aware_Z -0.012
## as.factor(brand_race)1 2.610
## as.factor(bias)1 1.453
## bias_aware_Z:as.factor(brand_race)1 -0.665
## bias_aware_Z:as.factor(bias)1 -0.020
## as.factor(brand_race)1:as.factor(bias)1 -0.740
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.291
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## bias_aware_Z 0.99070
## as.factor(brand_race)1 0.00952 **
## as.factor(bias)1 0.14730
## bias_aware_Z:as.factor(brand_race)1 0.50643
## bias_aware_Z:as.factor(bias)1 0.98384
## as.factor(brand_race)1:as.factor(bias)1 0.45995
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.77107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.971 on 294 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.04148, Adjusted R-squared: 0.01866
## F-statistic: 1.817 on 7 and 294 DF, p-value: 0.08359summary(lm(purchase ~ BMIS_calm_2*as.factor(brand_race)*as.factor(bias), wf_dt2)) # BA does not##
## Call:
## lm(formula = purchase ~ BMIS_calm_2 * as.factor(brand_race) *
## as.factor(bias), data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4348 -1.3228 0.1744 1.3861 3.5233
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.7381 0.9518 1.826
## BMIS_calm_2 0.5847 0.2991 1.955
## as.factor(brand_race)1 2.1967 1.2436 1.766
## as.factor(bias)1 1.5642 1.1364 1.376
## BMIS_calm_2:as.factor(brand_race)1 -0.4180 0.3944 -1.060
## BMIS_calm_2:as.factor(bias)1 -0.4102 0.3613 -1.135
## as.factor(brand_race)1:as.factor(bias)1 -1.8100 1.5519 -1.166
## BMIS_calm_2:as.factor(brand_race)1:as.factor(bias)1 0.4939 0.4985 0.991
## Pr(>|t|)
## (Intercept) 0.0689 .
## BMIS_calm_2 0.0516 .
## as.factor(brand_race)1 0.0784 .
## as.factor(bias)1 0.1698
## BMIS_calm_2:as.factor(brand_race)1 0.2901
## BMIS_calm_2:as.factor(bias)1 0.2572
## as.factor(brand_race)1:as.factor(bias)1 0.2445
## BMIS_calm_2:as.factor(brand_race)1:as.factor(bias)1 0.3226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.781 on 278 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.06422, Adjusted R-squared: 0.04066
## F-statistic: 2.726 on 7 and 278 DF, p-value: 0.0094736. Shop Intentions
summary(lm(shop_intentions_Z ~ as.factor(bias)*as.factor(brand_race), wf_dt2)) # main interaction working, it is defensiveness driving this effect, whe defensiveness is included as a term in this model the effect vanishes, but defensiveness on its own is still negatively correlated with the dv, it also loses its sig for shame and se, but they are not signifgicant predictors of the dv##
## Call:
## lm(formula = shop_intentions_Z ~ as.factor(bias) * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03843 -0.86980 0.06421 0.76362 1.92776
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4460 0.1038 -4.297 0.00002310
## as.factor(bias)1 0.4625 0.1495 3.094 0.00215
## as.factor(brand_race)1 0.6971 0.1526 4.568 0.00000707
## as.factor(bias)1:as.factor(brand_race)1 -0.4773 0.2162 -2.208 0.02800
##
## (Intercept) ***
## as.factor(bias)1 **
## as.factor(brand_race)1 ***
## as.factor(bias)1:as.factor(brand_race)1 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9624 on 314 degrees of freedom
## Multiple R-squared: 0.08251, Adjusted R-squared: 0.07374
## F-statistic: 9.412 on 3 and 314 DF, p-value: 0.000005659summary(lm(shop_intentions ~ shame_discrepancy , wf_dt2)) # increases in shame from t1 to t2 are associated with increases in shopping attentions, this effect is for across all of the conditions ##
## Call:
## lm(formula = shop_intentions ~ shame_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2249 -1.2249 -0.1547 1.2210 3.3995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2505 0.1066 39.858 <0.0000000000000002 ***
## shame_discrepancy 0.2670 0.1381 1.932 0.0543 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.8 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.01302, Adjusted R-squared: 0.009537
## F-statistic: 3.734 on 1 and 283 DF, p-value: 0.0543summary(lm(shop_intentions_Z ~ defensive_reverse_Z*as.factor(brand_race), wf_dt2)) # defensiveness does predict it, no interaction with brand race##
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8980 -0.3948 0.1070 0.6027 1.6065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04045 0.06938 0.583 0.56035
## defensive_reverse_Z -0.20333 0.07003 -2.903 0.00399
## as.factor(brand_race)1 0.31634 0.09848 3.212 0.00147
## defensive_reverse_Z:as.factor(brand_race)1 0.09775 0.09865 0.991 0.32265
##
## (Intercept)
## defensive_reverse_Z **
## as.factor(brand_race)1 **
## defensive_reverse_Z:as.factor(brand_race)1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8208 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.07025, Adjusted R-squared: 0.06007
## F-statistic: 6.901 on 3 and 274 DF, p-value: 0.0001701summary(lm(shop_intentions_Z ~ SE_change, wf_dt2)) # self-esteem change does not##
## Call:
## lm(formula = shop_intentions_Z ~ SE_change, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.98969 -0.58777 -0.08654 0.80260 1.31627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20023 0.05004 4.002 0.0000803 ***
## SE_change -0.04692 0.16408 -0.286 0.775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.846 on 284 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.0002878, Adjusted R-squared: -0.003232
## F-statistic: 0.08176 on 1 and 284 DF, p-value: 0.7751summary(lm(shop_intentions_Z ~ bias_discrepancy*as.factor(bias), wf_dt2)) # BD does not##
## Call:
## lm(formula = shop_intentions_Z ~ bias_discrepancy * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.96753 -0.54975 -0.03918 0.69974 1.67522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27664 0.08194 3.376 0.000838 ***
## bias_discrepancy -0.07834 0.04611 -1.699 0.090404 .
## as.factor(bias)1 -0.06287 0.13353 -0.471 0.638136
## bias_discrepancy:as.factor(bias)1 0.09510 0.06908 1.377 0.169694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8446 on 282 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.01075, Adjusted R-squared: 0.000223
## F-statistic: 1.021 on 3 and 282 DF, p-value: 0.3836summary(lm(shop_intentions_Z ~ bias_aware_Z, wf_dt2)) # BA does not##
## Call:
## lm(formula = shop_intentions_Z ~ bias_aware_Z, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.88858 -0.94082 -0.00988 0.45875 1.39600
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.094689 0.053897 1.757 0.080 .
## bias_aware_Z -0.003788 0.053986 -0.070 0.944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9366 on 300 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 1.641e-05, Adjusted R-squared: -0.003317
## F-statistic: 0.004924 on 1 and 300 DF, p-value: 0.9441summary(lm(SE_2 ~ shop_intentions_Z + as.factor(brand_race), wf_dt2)) # BMIS_calm does not##
## Call:
## lm(formula = SE_2 ~ shop_intentions_Z + as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.34586 -0.99447 0.06743 1.00553 2.48072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.83289 0.10930 25.919 < 0.0000000000000002 ***
## shop_intentions_Z 0.29499 0.09292 3.175 0.00167 **
## as.factor(brand_race)1 0.07586 0.15670 0.484 0.62869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.302 on 283 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.03834, Adjusted R-squared: 0.03154
## F-statistic: 5.641 on 2 and 283 DF, p-value: 0.0039617. Word of Mouth
summary(lm(wom_Z ~ as.factor(bias)*as.factor(brand_race), wf_dt2)) # main interaction working, it is defensiveness driving this effect, whe defensiveness is included as a term in this model the effect vanishes, but defensiveness on its own is still negatively correlated with the dv, it also loses its sig for shame and se, but they are not signifgicant predictors of the dv##
## Call:
## lm(formula = wom_Z ~ as.factor(bias) * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0582 -0.7747 0.1550 0.7199 2.0588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4693 0.1024 -4.584 0.000006596
## as.factor(bias)1 0.3870 0.1475 2.624 0.00911
## as.factor(brand_race)1 0.7853 0.1505 5.217 0.000000331
## as.factor(bias)1:as.factor(brand_race)1 -0.4010 0.2133 -1.880 0.06099
##
## (Intercept) ***
## as.factor(bias)1 **
## as.factor(brand_race)1 ***
## as.factor(bias)1:as.factor(brand_race)1 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9494 on 314 degrees of freedom
## Multiple R-squared: 0.1072, Adjusted R-squared: 0.09868
## F-statistic: 12.57 on 3 and 314 DF, p-value: 0.00000008821summary(lm(wom_Z ~ shame_discrepancy, wf_dt2)) # shame does not predict it neither does shame change, and it doesn't interact with anything to predict it##
## Call:
## lm(formula = wom_Z ~ shame_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.66782 -0.49641 -0.02045 0.46941 1.65472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19227 0.05057 3.802 0.000176 ***
## shame_discrepancy 0.10573 0.06551 1.614 0.107629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8537 on 283 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.009122, Adjusted R-squared: 0.00562
## F-statistic: 2.605 on 1 and 283 DF, p-value: 0.1076summary(lm(wom_Z ~ defensive_reverse_Z*as.factor(brand_race), wf_dt2)) # defensiveness does predict it##
## Call:
## lm(formula = wom_Z ~ defensive_reverse_Z * as.factor(brand_race),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.92602 -0.42021 0.08609 0.53949 1.75657
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.03836 0.06931 -0.553
## defensive_reverse_Z -0.15842 0.06996 -2.264
## as.factor(brand_race)1 0.46415 0.09837 4.718
## defensive_reverse_Z:as.factor(brand_race)1 0.04662 0.09855 0.473
## Pr(>|t|)
## (Intercept) 0.5804
## defensive_reverse_Z 0.0243 *
## as.factor(brand_race)1 0.0000038 ***
## defensive_reverse_Z:as.factor(brand_race)1 0.6365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.82 on 274 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.09728, Adjusted R-squared: 0.0874
## F-statistic: 9.843 on 3 and 274 DF, p-value: 0.000003472summary(lm(wom_Z ~ SE_change, wf_dt2)) # self-esteem change does not##
## Call:
## lm(formula = wom_Z ~ SE_change, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4919 -0.5102 -0.0340 0.4420 1.4087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19504 0.05073 3.844 0.000149 ***
## SE_change -0.01925 0.16637 -0.116 0.907976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8578 on 284 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 4.713e-05, Adjusted R-squared: -0.003474
## F-statistic: 0.01339 on 1 and 284 DF, p-value: 0.908summary(lm(wom_Z ~ bias_discrepancy, wf_dt2)) # BD does not##
## Call:
## lm(formula = wom_Z ~ bias_discrepancy, data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.52951 -0.50224 -0.02628 0.44968 1.49203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18792 0.05224 3.597 0.000379 ***
## bias_discrepancy -0.01507 0.02710 -0.556 0.578549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8574 on 284 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.001088, Adjusted R-squared: -0.002429
## F-statistic: 0.3093 on 1 and 284 DF, p-value: 0.5785summary(lm(wom_Z ~ bias_aware_Z*as.factor(brand_race)*as.factor(bias), wf_dt2)) # BA does not##
## Call:
## lm(formula = wom_Z ~ bias_aware_Z * as.factor(brand_race) * as.factor(bias),
## data = wf_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1145 -0.6867 0.2219 0.7105 1.7909
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.18449 0.11062
## bias_aware_Z 0.07858 0.10866
## as.factor(brand_race)1 0.49146 0.15482
## as.factor(bias)1 0.09996 0.15152
## bias_aware_Z:as.factor(brand_race)1 -0.15133 0.16211
## bias_aware_Z:as.factor(bias)1 -0.05385 0.14479
## as.factor(brand_race)1:as.factor(bias)1 -0.10521 0.21361
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.12039 0.21643
## t value Pr(>|t|)
## (Intercept) -1.668 0.09643 .
## bias_aware_Z 0.723 0.47015
## as.factor(brand_race)1 3.174 0.00166 **
## as.factor(bias)1 0.660 0.50995
## bias_aware_Z:as.factor(brand_race)1 -0.934 0.35133
## bias_aware_Z:as.factor(bias)1 -0.372 0.71023
## as.factor(brand_race)1:as.factor(bias)1 -0.493 0.62270
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1 0.556 0.57847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9228 on 294 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.05836, Adjusted R-squared: 0.03594
## F-statistic: 2.603 on 7 and 294 DF, p-value: 0.01278*** Correlation Matrices ****
colnames(wf_dt2)## [1] "ParticipantID" "StartDate"
## [3] "EndDate" "Status"
## [5] "Progress" "Duration (in seconds)"
## [7] "Finished" "RecordedDate"
## [9] "ResponseId" "DistributionChannel"
## [11] "UserLanguage" "consent"
## [13] "prolificID" "BA_1"
## [15] "BA_2" "BA_3"
## [17] "BA_4" "SE1"
## [19] "BMIS_sad_1" "BMIS_shame_1"
## [21] "BMIS_guilt_1" "BMIS_tired_1"
## [23] "BMIS_nervous_1" "BMIS_calm_1"
## [25] "BMIS_fedup_1" "BMIS_loving_1"
## [27] "BMIS_angry_1" "BMIS_lively_1"
## [29] "BMIS_caring_1" "BMIS_content_1"
## [31] "BMIS_gloomy_1" "BMIS_jittery_1"
## [33] "BMIS_drowsy_1" "BMIS_happy_1"
## [35] "Q1 RP1" "Q2 RP2"
## [37] "Q3 RP3" "Q4 RP4"
## [39] "Q5 RP5" "Q6 RP6"
## [41] "Q7 RP7" "Q8 RN1"
## [43] "Q9 RN2" "Q10 RN3"
## [45] "Q11 RN4" "Q12 RN5"
## [47] "Q13 RN6" "Q14 RN7"
## [49] "Q15 LP1" "Q16 LP2"
## [51] "Q17 LP3" "Q18 LP4"
## [53] "Q19 LP5" "Q20 LP6"
## [55] "Q21 LP7" "Q22 LN1"
## [57] "Q23 LN2" "Q24 LN3"
## [59] "Q25 LN4" "Q26 LN5"
## [61] "Q27 LN6" "Q28 LN7"
## [63] "spwtime_First Click" "spwtime_Last Click"
## [65] "spwtime_Page Submit" "spwtime_Click Count"
## [67] "attentioncheck_nb" "discrepancy_nb"
## [69] "BMIS_sad_2" "BMIS_shame_2"
## [71] "BMIS_guilt_2" "BMIS_tired_2"
## [73] "BMIS_nervous_2" "BMIS_calm_2"
## [75] "BMIS_fedup_2" "BMIS_loving_2"
## [77] "BMIS_angry_2" "BMIS_lively_2"
## [79] "BMIS_caring_2" "BMIS_content_2"
## [81] "BMIS_gloomy_2" "BMIS_jittery_2"
## [83] "BMIS_drowsy_2" "BMIS_happy_2"
## [85] "credibility" "objective"
## [87] "valid" "useful"
## [89] "rl_product_choice" "rl_shop_intentions"
## [91] "rl_purchase" "rl_wom"
## [93] "rr_product_choice" "rr_shop_intentions"
## [95] "rr_purchase" "rr_wom"
## [97] "attentioncheck_b" "discrepancy_b"
## [99] "SE_2" "age"
## [101] "race" "education"
## [103] "polit_affil" "polit_affil_4_TEXT"
## [105] "polit_affil_cont_1" "gender"
## [107] "gender_4_TEXT" "iat_prev"
## [109] "iat_racial" "iat_racial_time"
## [111] "iat_racial_quant" "recent_results"
## [113] "nobias_white" "nobias_black"
## [115] "bias_white" "bias_black"
## [117] "discrepancy_bias" "discrepancy_nobias"
## [119] "bias_discrepancy" "bias_discrepancy_centered"
## [121] "bias_discrepancy_Z" "condition"
## [123] "bias" "brand_race"
## [125] "shop_intentions" "shop_intentions_centered"
## [127] "shop_intentions_Z" "purchase"
## [129] "purchase_centered" "purchase_Z"
## [131] "wom" "wom_centered"
## [133] "wom_Z" "defensive"
## [135] "defensive_reverse" "defensive_reverse_centered"
## [137] "defensive_reverse_Z" "bias_aware"
## [139] "bias_aware_center" "bias_aware_Z"
## [141] "BMIS_guilt_1_center" "BMIS_guilt_1_Z"
## [143] "BMIS_shame_1_center" "BMIS_shame_1_Z"
## [145] "BMIS_sad_1_center" "BMIS_sad_1_Z"
## [147] "BMIS_guilt_2_center" "BMIS_guilt_2_Z"
## [149] "BMIS_shame_2_center" "BMIS_shame_2_Z"
## [151] "BMIS_sad_2_center" "BMIS_sad_2_Z"
## [153] "guilt_shame_sad" "guilt_shame_sad_center"
## [155] "guilt_shame_sad_Z" "shame_discrepancy"
## [157] "SE_2_Z" "SE_1_Z"
## [159] "SE_change"# Calculate correlation between variables
cor_matrix <- wf_dt2 %>%
select(
"BMIS_shame_2_Z",
"defensive_reverse_Z",
"bias_aware_Z",
"guilt_shame_sad_Z",
"SE_2_Z",
"shop_intentions",
"wom",
"purchase",
"polit_affil_cont_1",
"rl_shop_intentions",
"rl_purchase",
"rl_wom",
"rr_shop_intentions",
"rr_purchase",
"rr_wom") %>%
cor(use = "pairwise.complete.obs")
# Create a function to calculate p-values
get_p_value <- function(x, y) {
cor_test <- cor.test(x, y, method = "pearson")
return(cor_test$p.value)
}
# Calculate p-values for the correlations
p_values <- outer(colnames(cor_matrix), colnames(cor_matrix),
Vectorize(function(x, y) get_p_value(cor_matrix[, x], cor_matrix[, y])))
# Create a dataframe for the correlation values and p-values
corr_data <- data.frame(variables = rep(colnames(cor_matrix), each = ncol(cor_matrix)),
others = rep(colnames(cor_matrix), ncol(cor_matrix)),
corr_values = as.vector(cor_matrix),
p_value = as.vector(p_values))
# Create a function to add asterisks based on p-values
add_asterisk <- function(p_value) {
if (p_value < 0.05) {
return("*")
} else {
return("")
}
}
# Apply the function to generate the asterisk labels
corr_data$asterisks <- sapply(corr_data$p_value, add_asterisk)
# Create a heatmap for the correlation matrix with labels and significance asterisks
heatmap_plot <- ggplot(data = corr_data, aes(x = variables, y = others, fill = corr_values)) +
geom_tile() +
geom_text(aes(label = sprintf("%.2f", corr_values)), color = "black", size = 3) + # Display correlation values as labels
geom_text(aes(label = asterisks), color = "red", size = 5) + # Display significance asterisks
scale_fill_gradient2(low = "blue", mid = "white", high = "red",
midpoint = 0, limits = c(-1, 1)) +
labs(title = "Correlation Heatmap",
x = "Variables",
y = "Other Variables") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display the heatmap
print(heatmap_plot)
# Install and load the writexl package if not already installed
# install.packages("writexl")
library(writexl)
# Specify the file path where you want to save the Excel file
excel_file_path <- "correlation_matrix.xlsx"
# Write the correlation matrix to an Excel sheet
write_xlsx(corr_data, path = excel_file_path)
# Display the file path to confirm where the Excel file is saved
excel_file_path## [1] "correlation_matrix.xlsx"